TY - GEN
T1 - Facial video age progression considering expression change
AU - Yamamoto, Shintaro
AU - Savkin, Pavel A.
AU - Kato, Takuya
AU - Furukawa, Shoichi
AU - Morishima, Shigeo
N1 - Funding Information:
This work was supported in part by JST ACCEL Grant Number JPMJAC1602, Japan.
Publisher Copyright:
© 2017 ACM.
PY - 2017/6/27
Y1 - 2017/6/27
N2 - This paper proposes an age progression method for facial videos. Age is one of the main factors that changes the appearance of the face, due to the associated sagging, spots, and wrinkles. These aging features change in appearance depending on facial expressions. As an example, we see wrinkles appear in the face of the young when smiling, but the shape of wrinkles changes in older faces. Previous work has not considered the temporal changes of the face, using only static images as databases. To solve this problem, we extend the texture synthesis approach to use facial videos as source videos. First, we spatio-temporally align the videos of database to match the sequence of a target video. Then, we synthesize an aging face and apply the temporal changes of the target age to the wrinkles appearing in the facial expression image in the target video. As a result, our method successfully generates expression changes specific to the target age.
AB - This paper proposes an age progression method for facial videos. Age is one of the main factors that changes the appearance of the face, due to the associated sagging, spots, and wrinkles. These aging features change in appearance depending on facial expressions. As an example, we see wrinkles appear in the face of the young when smiling, but the shape of wrinkles changes in older faces. Previous work has not considered the temporal changes of the face, using only static images as databases. To solve this problem, we extend the texture synthesis approach to use facial videos as source videos. First, we spatio-temporally align the videos of database to match the sequence of a target video. Then, we synthesize an aging face and apply the temporal changes of the target age to the wrinkles appearing in the facial expression image in the target video. As a result, our method successfully generates expression changes specific to the target age.
KW - Age progression
KW - Facial video
KW - Video editing
UR - http://www.scopus.com/inward/record.url?scp=85025435132&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85025435132&partnerID=8YFLogxK
U2 - 10.1145/3095140.3095145
DO - 10.1145/3095140.3095145
M3 - Conference contribution
AN - SCOPUS:85025435132
T3 - ACM International Conference Proceeding Series
BT - CGI 2017 - Proceedings of the 2017 Computer Graphics International Conference
PB - Association for Computing Machinery
T2 - 2017 Computer Graphics International Conference, CGI 2017
Y2 - 27 June 2017 through 30 June 2017
ER -